• So you've trained a cool machine learning model - now what?
    Hello Makers! Join us this evening to hear from makers behind H2O Driverless AI and Technical Staff of Oracle! following is a brief agenda for the evening: 6 - 6:30 - Doors open and networking 6:30 - 7:30 - Anthony and Mark's talk 7:30 - 8 - Q&A Talk 1: AlphaZero on GraphPipe In this introductory discussion, X from Oracle Cloud Infrastructure will walk through the essential elements of taking neural network models from R&D to production. The discussion will include a survey of prominent model formats, including Tensorflow, Caffe2, ONNX, and TensorRT, and discuss how one can deploy these models to production using various serving technologies like GraphPipe. AlphaZero on GraphPipe - Accelerated training of the AlphaZero algorithm using GraphPipe AlphaZero is an interesting ML case study, as it requires massive amounts of model inference for its game generation phase. In this talk, we discuss the challenges and bottlenecks of the AlphaZero algorithm, and go into detail about how we used GraphPipe at a variety of key points in our architecture to overcome them, from initial AlphaZero training all the way through front-end web-based application deployment. Talk 2: Interpretable Machine Learning The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Speaker Bios: Speaker 1 Anthony is a Consulting Member of Technical Staff at Oracle. He works in OCI where he focuses on the intersection of distributed computing and AI. Previous to Oracle, he was Principal Engineer at Whitepages, where he architected data products, created ML-driven fraud detection infrastructure, and initiated their migration from Data Center to cloud. Anthony was also on the founding team of OpenStack, and worked as a core developer on Cinder, Horizon, and Devstack. Speaker 2 Mark is a hacker at H2O. He was previously in the finance world as a quantitative research developer at Thomson Reuters and Nipun Capital. He also worked as a data scientist at an IoT startup, where he built a web based machine learning platform and developed predictive models. Mark has a MS Financial Engineering from UCLA and a BS Computer Engineering from University of Illinois Urbana-Champaign. In his spare time Mark likes competing on Kaggle and cycling.

    2301 Leghorn Street

    2301 Leghorn St · Mountain View, CA

    3 comments
  • H2O AI World London 2018
    Note: Please get your free livestream tickets here: https://www.eventbrite.com/e/h2o-ai-world-london-2018-tickets-48016161632 Hello Makers! We're headed across the pond for our first H2O AI World London! Join the greatest minds in AI and data science for this 2-day interactive event packed with deep-dive technical sessions, talks on real-world business use cases and a hands-on training. You'll discover the strategies and insights you need to optimize and transform your business and prepare for the wave of AI. H2O AI World London is a must-attend event whether you're a newbie getting your toes wet, or an H2O power user. You'll get to network with industry trailblazers and connect with your peers who are shaping the future of AI and machine learning. Following is the agenda for the conference: October 30, 2018 The conference will feature talks and technical sessions from all walks of our community: Makers, Industry Leaders, Data Scientists, Kaggle Grandmasters, and machine learning enthusiasts alike. We have a number of panels to fill your data science appetite including Women in Data Science and Inclusion and Meet the Kaggle Grandmasters. The day culminates with a reception including our infamous H2O themed cocktails, DJ and a book signing. Space is limited so be sure to register early to save your seat at the AI education destination of the year. #H2OAIWorld

    London Hilton on Park Lane

    22 Park Ln · Greater London

  • Explain, Explore and Visualise Black Box Models with DALEX
    Hello Makers! Join us this evening to discuss machine learning interpretability! Following is a brief agenda for the evening: 6:00 - 6:30 PM: Doors open for networking and pizza 6:30 - 7:15 PM: Przemyslaw's talk 7:15 - 7:30 PM: Q&A Description: Why do you need tools for explanation of model predictions? How to use such tools? Which one should you choose? During the talk I will overview and compare the popular approaches to local explanations of predictive models - 3 different implementations of LIME (lime https://cran.r-project.org/web/packages/lime/index.html; live https://cran.r-project.org/web/packages/live/ and iml https://cran.r-project.org/web/packages/iml/), - Break Down for interactions (https://github.com/pbiecek/breakDown), - Ceteris Paribus profiles (https://github.com/pbiecek/ceterisParibus ), - Shapley Values (as in iml package). I also show how to work with the DALEX: a uniform toolbox for exploration and comparisons of machine learning models https://github.com/pbiecek/DALEX. Examples will be in R, but similar methods exist for python and other popular languages. Przemyslaw Biecek is an Associate Professor at Warsaw University of Technology / PL). More information regarding him can be found at his Linkedin page: https://www.linkedin.com/in/przemyslaw-biecek-5b41761/

    2301 Leghorn Street

    2301 Leghorn St · Mountain View, CA

    1 comment
  • Dive into H2O: Training + Workshop
    Join us for a day of training in the South Bay at NVIDIA’s headquarters. Please note, your RSVP on meetup.com will not count towards your ticket. To save a spot, please register here: https://www.eventbrite.com/e/dive-into-h2o-training-workshop-tickets-50127590974 This training and workshop are geared towards the data science practitioner wanting to dive deep into H2O. Sessions will include a hands-on lab with our groundbreaking product, H2O Driverless AI which automates machine learning and the H2O open source platform trusted by over 130,000 data scientists and 13,000+ organizations across the globe. The makers behind these revolutionary products will be on deck to answer your questions. Don't forget to bring your laptops and power cords. Agenda: 8AM - 9AM - Registration & Breakfast 9AM - 12PM - H2O Driverless AI Training - Machine Learning Interpretability - Feature Engineering - Auto-Visualization - Hands-on lab 12PM - 1PM - Lunch 1PM - 3PM - H2O-3 and Sparkling Water Training 3PM - Training ends Come for the learning, stay for the fun! Patrick Aboyoun will be leading the sessions. Here is more about Patrick: Patrick has made a career out of creating and delivering software and training for data scientists, particularly those who love R. He has worked on Oracle R Enterprise at Oracle, RevoScaleR at Revolution Analytics, Bioconductor at Fred Hutchinson Cancer Research Center, and S-PLUS at Insightful Corporation (now part of the Spotfire division of TIBCO). Just prior to joining H2O.ai, he spent a year at an e-commerce company where he used H2O to drive marketing decisions. Patrick received an M.S. in Statistics from the University of Washington and a B.S. in Statistics from Carnegie Mellon University.

    Nvidia Endeavor

    2788 San Tomas Expressway · Santa Clara, CA

    1 comment
  • Introduction to Deep Learning, Keras, and TensorFlow
    Hello Makers! Following up to Oswald's talk from early this year, we're hosting another session on the fundamentals! Agenda: 6:00 - 6:30 PM: Doors open for networking and pizza 6:30 - 7:15 PM: Oswald's talk 7:15 - 7:30 PM: Q&A Description: This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.) Oswald's Bio: Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world. He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.

    H2O.ai

    2301 Leghorn Street · Mountain View, CA

    8 comments
  • AI in Financial Services–Final Mile of a Debit Card Fraud Machine Learning Model
    Hello Makers! Join us this evening to hear from Wells Fargo's Daniel Dixon on AI in Financial Services. Following is a brief agenda for the meetup: 6:00 - 6:30 PM : Doors open & Networking 6:30 - 7:30 PM : Daniel's Talk 7:30 - 8:00 PM : Q&A Talk's Description: Today, credit and debit card fraud detection machine learning models are a critical component of a financial institution’s fraud mitigation operations. Predictive performance of these models is extremely important to help catch fraudsters and shut down a customer’s compromised card as soon as possible. Because of this, data scientists often focus all efforts on the training phase of the model life cycle, trying to squeeze out as much predictive power as possible. In highly regulated U.S. banks, and really anywhere one is deploying machine learning models for critical business results, carefully delivering the models that final mile into production can be just as important. In this talk, we explore two ways data scientists can help deliver in the final mile: Gradient Boosting Machine (GBM) fraud model interpretability and model monitoring. Speaker's Bio: Daniel Dixon is a senior data engineer on the Enterprise Analytics & Data Science team at Wells Fargo, where he is responsible for designing and building scalable, big data pipelines to feed intelligent systems across the bank. In this role he specializes in big data and advanced analytic challenges, utilizing machine learning, statistics, process optimization, and visualization techniques to analyze and assemble large, complex datasets. Prior to joining Wells Fargo in 2014, Daniel spent five years as a professional services consultant for Teradata with a focus on visualization and ETL technologies. He holds a Bachelor of Science in Electrical Engineering with a minor in Computer Science from the Georgia Institute of Technology. LinkedIn: www.linkedin.com/in/danielbdixon

    2301 Leghorn Street

    2301 Leghorn St · Mountain View, CA

    1 comment
  • ICLR 2018 Recap
    Hello Makers! Join us for another great meetup with Simon Kozlov, Machine Learning Engineer at Instrumental as he gives a recap of the sixth annual ICLR (International Conference on Learning Representations) 2018 a niche deep learning conference whose focus is to study how to learn representations of data, which is basically what deep learning does. Following is a brief agenda: 6:00 - 6:30 PM : Doors open & Networking 6:30 - 7:30 PM : Simon's Talk 7:30 - 8:00 PM : Q&A We will go through some themes and some papers from ICLR 2018 conference, which happened this May in Vancouver. It's been a while, but better late than never! In particular, we'll go through the advances in: - GAN training - Adversarial examples - New building blocks for deep learning - Learning more effectively with less labeled data - New domains to apply deep learning techniques A good way to get a download of some fun papers if you missed the conference! Simon's Bio: Simon Kozlov is a Machine Learning Engineer at Instrumental focusing on applying deep learning techniques to the manufacturing space. Prior to Instrumental, he was part of the machine learning team at Dropbox. Before that, he helped make deep learning possible by giving a reason for GPUs to exist - in other words, worked on games and real-time rendering. https://www.linkedin.com/in/sim0nsays/

    2301 Leghorn Street

    2301 Leghorn St · Mountain View, CA

    4 comments
  • Introduction to Machine Learning
    This is an absolute beginner's guide to machine learning for the enterprise. Come join our strategic and design-thinking inspired conversation for beginners to explore and apply machine learning at your enterprise! Learn about the current state of machine learning and AI at one of the world's largest enterprises, IBM. In addition as this is a joint event with H2O we will also be covering an introduction to H2O, one of the leading open-source AI platforms, for people taking their first steps into the world of machine learning and big data. The presentation will provide an overview of the challenges you can tackle with H2O – classification, regression, anomaly detection, etc. – and how to download and start using H2O today. Whether you love coding or hate it, H2O has an interface that can cater to your preference (it has an R API, Python API, and web-based UI). There are no requirements to get started, just the desire to get your hands wet (with H2O). Schedule: 6:00-6:15pm: Dinner and registration 6:15-8:30pm: Current state of Machine Learning and AI at IBM Challenges you can tackle with H2O Requirements No experience is necessary.

    2301 Leghorn Street

    2301 Leghorn St · Mountain View, CA

    3 comments
  • Simplifying Feature Engineering & model tuning, ensembling & deployment w H2O
    Description: H2O.ai is democratizing AI by automating machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy. Now, data scientists of all proficiency levels can train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client API from Python. Driverless AI builds hundreds or thousands of models under the hood to select the best feature engineering recipes for your specific problem. To speed up training, H2O Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, we can now run orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on premise. There are two more product innovations: statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and the models. Speaker's Bio: Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

    San Jose Convention Centre

    150 W San Carlos St · San Jose, CA

  • GPU Accelerated Machine Learning on the Cloud - NVIDIA, AWS, H2O.ai, MapD
    Note: RSVP'ing on this meetup page doesn't account for your free ticket to the event. Please grab your free spot here: https://www.eventbrite.com/e/gpu-accelerated-machine-learning-on-the-cloud-nvidia-aws-h2oai-mapd-tickets-46509191243 [Agenda] 1:00-1:25 - Registration 1:25-1:30 - Welcome and Introductions 1:30-1:45 - GPU accelerated machine learning applications Richard Salazar, Senior Business Development Manager, NVIDIA 1:45-2:00 - Introduction to AWS machine learning services and Amazon EC2 P3 Instances Pratap Ramamurthy, Partner Solutions Architect, AWS 2:00-3:00 - Driverless AI - Introduction to Automated Machine Learning for the Enterprise Vinod Iyengar, Director of Partnerships and Alliances, H2O.ai 3:00-3:15 - Break 3:15-3:30 - H2O4GPU (GPU Accelerated Open Source Machine Learning) Vinod Iyengar, Director of Partnerships and Alliances, H2O.ai 3:30-4:15 - End-to-end machine learning with the GPU Open Analytics Initiative (GOAI) Ashish Bambroo, VP of Business Development, MapD 4:15-5:00 - Food & Networking - - - [Speakers] 1) Richard Salazar, Senior Business Development Manager, NVIDIA Richard Salazar is a veteran NVIDIA Business Development Executive with over 18 years to experience in the company. 2) Pratap Ramamurthy, Partner Solutions Architect, AWS Pratap works with AWS partners, helping them build their solutions on top of AWS. He works with several partners, especially partners that specialize in big data, machine learning, and AI-based solutions. Prior to AWS, he was a researcher when he worked on creating quantum dot lasers, created test tools for web servers and optimized WiFi using game theory. Currently, he focuses on Machine Learning especially Natural Language Processing (NLP) and Reinforcement Learning. 3) Vinod Iyengar, Director of Partnerships and Alliances, H2O.ai Vinod heads partnerships and alliances and works with all the strategic partners, channels, and resellers. He is a data scientist by training and is responsible for driving the product and messaging. Prior to H2O, Vinod was an early employee at Activehours and helped create the data science models behind the innovative payments company. Vinod holds a Masters in Quantitative Analysis from Univ of Cincinnati and a BS in Engineering from Mumbai University. 4) Ashish Bambroo, VP of Business Development, MapD Ashish is responsible for all global partnerships, channels, and resellers. Most recently, Ashish was Sr. Director at MuleSoft where he led embedded and OEM partnerships with MuleSoft's largest partners. Prior to that, Ashish was at Intuit where he held senior management positions, most recently as GM of QuickBooks Hosting where he built a new growth engine for the company. Ashish holds an MBA from UCLA, and a BS in Electrical Engineering from NIT, India.

    Amazon Web Services (AWS)

    475 Sansome St. 10th Fl · San Francisco, CA

    7 comments